BiloKey : A Scalable Bi-Index Locality-Aware In-Memory Key-Value Store

Abstract

Fast in-memory key value stores are the keys to building large-scale Internet services. The state-of-the-art solutions mainly focus on optimizing the performance for read-intensive workloads. Nevertheless, a wide range of applications demonstrate a significant amount of updates and range queries, which scale poorly with the current implementations. In this paper, we present BiloKey, a highly scalable in-memory key value store on multi-core machines, significantly outperforming Redis and Memcached for a variety of mixed read and write workloads. To achieve this, BiloKey leverages a fast bi-index comprised by a Hash Table index and a SkipList index, where the former supports feature rich operations including GET, UPDATE and DELETE with $O$O(1) complexity, while the latter supports SCAN with $O$O(log$N$N) complexity. Furthermore, to make the bi-index design scale well, BiloKey adopts three techniques: lazy synchronization for reducing the overhead of maintaining index consistency, lock-free data structure for supporting multi-writers, and locality-aware data parallel processing for preserving the data locality of requests. Compared with two popular in-memory KV stores (i.e., Redis and Memcached), experimental results show that: (1) for write-intensive workloads, BiloKey outperforms Redis and Memcached by 7.8x and 3.7x on average (up to 11.5x and 4.8x), respectively; (2) for scan-intensive workloads, BiloKey achieves an average speedup of 2.3x against Redis; (3) for read-intensive workloads, BiloKey also outperforms Redis and Memcached by 1.2x and 1.8x on average.